Alireza Rezvanian School of Computer Science, Institute for - PowerPoint PPT Presentation

یهوژپ تسایس و یراگن هدنیآ یشهوژپ هورگ هاگشهوژپ یصصخت یاهرانیمسورین 17 تشهبیدرا1397 هکبش یعامتجانيصصختم Alireza Rezvanian School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran Future Studies Department, Niroo Research Institute (NRI), Tehran, Iran Network analysis and data mining consultant Ph.D. Computer engineering (artificial intelligence) rezvanian@gmail.com

Outline 2 • Basic concepts – Complex networks, online social networks and social media • Social networks – Issues, problems, applications • Social network analysis • Enterprise social networks • Social network for experts • Case studies Social networks for experts, Alireza Rezvanian, Niroo Research Institute (NRI), May 2018.

Basic concepts 3 • Many real systems represent/model as a network (graph) • Complex network: is a graph (network) with non-trivial topological features — features that do not occur in simple networks such as lattices or random graphs but often occur in graphs modelling of real systems. – Neural, technological, biological, social, information, etc. • Online social networks: Online tools (web or app) for social services based on user activities/interactions • Social media : are computer-mediated technologies that facilitate the creation and sharing of information, ideas, career interests and other forms of expression via virtual communities and networks. • Social network: is a social structure made up of a set of social actors (e.g., individuals or organizations), sets of dyadic ties, and other social interactions between actors. Social networks for experts, Alireza Rezvanian, Niroo Research Institute (NRI), May 2018.

Complex networks 4 Social network Neural network Financial network Internet Emails Airport network Biological network Movies and Actors Social networks for experts, Alireza Rezvanian, Niroo Research Institute (NRI), May 2018.

Complex and social networks 5 • Complex networks – Common universal properties unlike simple networks – Small world phenomena – Scale free network – Modular/ community structures – Hub nodes – Pattern or motifs • Social networks – Large size – Dynamic network – Various contents – Different user activities – Information sharing – Influence spread Social networks for experts, Alireza Rezvanian, Niroo Research Institute (NRI), May 2018.

7 Source: https://www.smartinsights.com/social-media-marketing/social-media-strategy/new-global-social-media-research/ Social networks for experts, Alireza Rezvanian, Niroo Research Institute (NRI), May 2018.

Social network issues 8 • Top websites by traffic Sources: https://www.alexa.com/topsites Site Rank (Alexa) Type Description YouTube 2 Video sharing User-submitted videos with rating, comments, and contests. Facebook 3 Social network A social utility that connects people, to keep up with friends, upload photos, share links and videos. Reddit 6 Social news and entertainment User-generated news links. Votes promote stories to the front page. Twitter 13 Social network Social networking and microblogging service utilizing instant messaging, SMS or a web interface. photo and video-sharing social networking service Instagram 15 Photo sharing and social media VK 17 Social network Russian Social network Sina Corp 19 Portal and instant messaging China's famous IM provider. LinkedIn 34 Employment-oriented Social network A networking tool to find connections to recommended job candidates, industry experts and business partners. Allows registered users to maintain a list of contact details of people they know and trust in business. Social networks for experts, Alireza Rezvanian, Niroo Research Institute (NRI), May 2018.

Number of social network users worldwide from 2010 to 2021 (in billions) 9 Source: https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/ Social networks for experts, Alireza Rezvanian, Niroo Research Institute (NRI), May 2018.

Most popular social networks worldwide as of April 2018, ranked by number of active users (in millions) 10 Source: https://www.statista.com/statistics/272014/gl obal-social-networks-ranked-by-number-of- users/ Social networks for experts, Alireza Rezvanian, Niroo Research Institute (NRI), May 2018.

Average number of social media accounts per internet user from 2013 to 2017 13 Source: https://www.statista.com/statistics/788084/number-of-social-media-accounts/ Social networks for experts, Alireza Rezvanian, Niroo Research Institute (NRI), May 2018.

Average number of social media accounts per internet user as of 2nd quarter 2017, by age group 14 https://www.statista.com/statistics/381964/number-of-social-media-accounts/ Social networks for experts, Alireza Rezvanian, Niroo Research Institute (NRI), May 2018.

What Is Social Network Analysis? 17 “Social network analysis (SNA) is the mapping and measuring of relationships and flows between people, groups, organizations, computers or other information/knowledge processing entities.” Liebowitz J. Thoughts on Knowledge Sharing & Knowledge Gaps for Improved Strategic Human Capital Planning. 2008. “The technique empirically measures – how the network is structured, – and through interpretation suggest how the structural properties may affect the behavior of participants.” Merrill J, et al. Findings from an organizational network analysis to support local public health management. J Urban Health . 2008. 85(4): 572-84. Social networks for experts, Alireza Rezvanian, Niroo Research Institute (NRI), May 2018.

Network analysis 18 18 Data Graph Process Analysis collection extraction Data collection Graph extraction Social networks for experts, Alireza Rezvanian, Niroo Research Institute (NRI), May 2018.

Some Key Problems 19 • Modeling: Which model or representation is suitable for modeling network structures and dynamical analysis? • Centrality: To what degree is a given node central to the network? • Link Prediction: Which edges not currently in the network are most likely to form? • Community Detection: How can the nodes be clustered into natural or useful groups? • Information Diffusion: How does information diffuse over the network? Social networks for experts, Alireza Rezvanian, Niroo Research Institute (NRI), May 2018.

Network models 20 Random graph (ER) - (Erdos 1960) – يفداصت فارگ ( ER فارگشودرا- ينير( – کچوک يايند فارگ ( WS ستاو فارگ- ستاگورتسا( Small world model (WS) - (Watts 1998) – سايقم زا لقتسم فارگ ( BA فارگيساباراب- تربلا( Scale free network (BA) - (Barabasi 1999) – هناميپ فارگيا ( GN1 فارگناوريگ- نموين1( Modular networks – (CN) – (Girvan 2002) – GN2 ناوريگ فارگ- نموين 2 ( فارگييايفارغج ) Geographic networks (GN) - (Gastner 2006) Kronecker graph (KG) – (Leskovec 2010 ) Multiplicative (MAG) – ( Kim 2012) Social networks for experts, Alireza Rezvanian, Niroo Research Institute (NRI), May 2018.

Centrality 21 • Centrality is a measure of the importance of a node, i.e., how central it is to the network • Can be measured in different ways, depending on context – In practice may want to combine several methods • May require a (cheap) local computation, or a (very expensive) global computation • Centrality measures – Degree, betweenness, closeness, PageRank, Bonacic , …. Social networks for experts, Alireza Rezvanian, Niroo Research Institute (NRI), May 2018.

Small-world experiment 22 Milgram’s experiment ( 1960 ’s) : Given a target individual and a particular property, pass the message to a person you correspond with who is “closest” to the target. • Start – Omaha, Nebraska, and Wichita, Kansas • End – Boston, Massachusetts Social networks for experts, Alireza Rezvanian, Niroo Research Institute (NRI), May 2018.

The 20-80 Rule 23 • It’s a common “way of saying” – But it has scientific foundations – For all those systems that follow a power law distribution • Examples – The 20% of the Web sites gests the 80% of the visits (actual data: 15%-85%) – The 20% of the Internet routers handles the 80% of the total Internet traffic – The 20% of world industries hold the 80 % of the world’s income – The 20% of the world population consumes the 80% of the world’s resources – The 20% of the Italian population holds the 80% of the lands (that was true before the Mussolini fascist regime, when lands redistribution occurred) – The 20% of the earthquakes caused the 80% of the victims – The 20% of the rivers in the world carry the 80% of the total sweet water – The of the proteins handles the of the most critical metabolic processes • Does this derive from the power law distribution? YES! Social networks for experts, Alireza Rezvanian, Niroo Research Institute (NRI), May 2018.

Scale Free Networks are Fractals? 24 • Yes, in fact: – They are the same at whatever dimension we observe them – Also, the fact that they grow according to a power law can be considered as a sort of fractal dimension of the network … • Having a look at the figures clarifies the analogy Social networks for experts, Alireza Rezvanian, Niroo Research Institute (NRI), May 2018.

Hub nodes 25 • Few hub nodes Social networks for experts, Alireza Rezvanian, Niroo Research Institute (NRI), May 2018.